Jin Guo

Jörg Kienzle

2022-10-24

Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems (published)

Characterizing User Behaviors in Open-Source Software User Forums: An Empirical Study

Jazlyn Hellman

Jiahao Chen

Md. Sami Uddin

Jinghui Cheng

User forums of Open Source Software (OSS) enable end-users to collaboratively discuss problems concerning the OSS applications. Despite deca… (see more)des of research on OSS, we know very little about how end-users engage with OSS communities on these forums, in particular, the challenges that hinder their continuous and meaningful participation in the OSS community. Many previous works are developer-centric and overlook the importance of end-user forums. As a result, end-users' expectations are seldom reflected in OSS development. To better understand user behaviors in OSS user forums, we carried out an empirical study analyzing about 1.3 million posts from user forums of four popular OSS applications: Zotero, Audacity, VLC, and RStudio. Through analyzing the contribution patterns of three common user types (end-users, developers, and organizers), we observed that end-users not only initiated most of the threads (above 96% of threads in three projects, 86% in the other), but also acted as the significant contributors for responding to other users' posts, even though they tended to lack confidence in their activities as indicated by psycho-linguistic analyses. Moreover, we found end-users more open, reflecting a more positive emotion in communication than organizers and developers in the forums. Our work contributes new knowledge about end-users' activities and behaviors in OSS user forums that the vital OSS stakeholders can leverage to improve end-user engagement in the OSS development process.

2022-07-19

Proceedings of the 15th International Conference on Cooperative and Human Aspects of Software Engineering (published)

GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network

Mohammad Amin Mozaffari

Xinyuan Zhang

Jinghui Cheng

Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and technique… (see more)s that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.

2022-04-29

CHI Conference on Human Factors in Computing Systems (published)

Aspirations and Practice of ML Model Documentation: Moving the Needle with Nudging and Traceability

Avinash Bhat

Austin Coursey

Grace Hu

Sixian Li

Nadia Nahar

Shurui Zhou

Christian Kästner

The documentation practice for machine-learned (ML) models often falls short of established practices for traditional software, which impede… (see more)s model accountability and inadvertently abets inappropriate or misuse of models. Recently, model cards, a proposal for model documentation, have attracted notable attention, but their impact on the actual practice is unclear. In this work, we systematically study the model documentation in the field and investigate how to encourage more responsible and accountable documentation practice. Our analysis of publicly available model cards reveals a substantial gap between the proposal and the practice. We then design a tool named DocML aiming to (1) nudge the data scientists to comply with the model cards proposal during the model development, especially the sections related to ethics, and (2) assess and manage the documentation quality. A lab study reveals the benefit of our tool towards long-term documentation quality and accountability.

2022-04-13

ArXiv (preprint)

How Do Open Source Software Contributors Perceive and Address Usability?: Valued Factors, Practices, and Challenges

Wenting Wang

Jinghui Cheng

Given the recent changes in the open source software (OSS) landscape, we examined OSS contributors’ current valued factors, practices, and… (see more) challenges concerning usability. Our survey provides insights for OSS practitioners and tool designers to promote a user-centric mindset and improve usability practice in OSS communities.

2022-02-01

IEEE Software (published)

Automated, interactive, and traceable domain modelling empowered by artificial intelligence

Rijul Saini

Gunter Mussbacher

Jörg Kienzle

2022-01-08

Software and Systems Modeling (published)

Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability

Avinash Bhat

Austin Coursey

Grace Hu

Sixian Li

Nadia Nahar

Shurui Zhou

Christian KÃ¤stner

2022-01-01

(published)

Deposited in DRO : 17 January 2022 Version of attached le : Accepted Version Peer-review status of attached

Nelly Bencomo

Rachel Harrison

Hans-Martin Heyn

Tim Menzies

Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowl… (see more)edge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software. Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowledge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software1. To begin, we first need a definition. What is requirements engineering or RE? RE used to be viewed as an early lifecycle activity that proceeded analysis, design, coding and testing. For safety critical applications there is certainly a pressing need to create those requirements before the coding starts (we will return to this point, later in the paper). However, in this age of DevOps and Autonomous and Self-adaptive systems, requirements can happen at many other times in a software project[15], [14]. We say that: Requirements engineering is any discussion about what to build and how to trade-off competing cost/benefits. It can happen before, during, or after runtime. 1This paper is based on the Panel “Artificial Intelligence and Requirement Engineering: Challenges and Opportunities”, which took place at the Eighth International Workshop on Artificial Intelligence and Requirements Engineering (AIRE). As shown in Table 1 and Table 2, there are many ways AI can help RE, across a broad range of SE activities. But, what about the other way around? If we add more requirements into AI, and use RE methods to get truly desired requirements, can we make better software by combining human and artificial intelligence? In our view, when integrating AI into software engineering is a co-design problem between humans, the AI model, the data required to train and validate the desired behaviour, and the hardware running the AI model, in addition to the classical software components. This means that when integrating AI, you need to know and understand the context of the system in which you want to apply your AI model to derive the necessary model requirements [17]. For example, in the arena of safety critical systems, model construction must be guided by safety requirements. one challenge for AI in RE are safety standards that base on the EN-IEC 61508 standard2. These safety standards assume that for software only systematic faults exists. Therefore, they emphasise correct processes and the creation of lifecycle artifacts to minimise systematic mistakes during both the 2Functional Safety of Electrical/Electronic/Programmable Electronic Safety-related Systems; for example ISO 26262 for the automotive sector or IEC 61511 for the process industry. IEEE Software (submitted) Published by the IEEE Computer Society © 2021 IEEE 1

2022-01-01

(published)

www.semanticscholar.org

GitHub repositories with links to academic papers: Public access, traceability, and evolution

Supatsara Wattanakriengkrai

Bodin Chinthanet

Hideaki Hata

Raula Gaikovina Kula

Christoph Treude

Kenichi Matsumoto

2022-01-01

Journal of Systems and Software (published)

The Secret to Better AI and Better Software (Is Requirements Engineering)

Nelly Bencomo

Rachel. Harrison

Hans-Martin Heyn

Tim J Menzies

Recently, practitioners and researchers met to discuss the role of requirements, and AI and SE. We offer here notes on that fascinating disc… (see more)ussion. Also, have you considered writing for this column? This “SE for AI” column publishes commentaries on the growing field of SE for AI. Submissions are welcomed and encouraged (1,000–2,400 words, each figure and table counts as 250 words, try to use fewer than 12 references, and keep the discussion practitioner focused). Please submit your ideas to me at timm@ieee.org.—Tim Menzies

2022-01-01

IEEE Software (published)

The Secret to Better AI and Better Software (Is Requirements Engineering)

Nelly Bencomo

Rachel Harrison

Hans-Martin Heyn

Tim Menzies

2022-01-01

IEEE Software (published)

Splitting, Renaming, Removing: A Study of Common Cleaning Activities in Jupyter Notebooks

Helen Dong

Shurui Zhou

Christian KÃ¤stner

Data scientists commonly use computational notebooks because they provide a good environment for testing multiple models. However, once the … (see more)scientist completes the code and finds the ideal model, he or she will have to dedicate time to clean up the code in order for others to easily understand it. In this paper, we perform a qualitative study on how scientists clean their code in hopes of being able to suggest a tool to automate this process. Our end goal is for tool builders to address possible gaps and provide additional aid to data scientists, who then can focus more on their actual work rather than the routine and tedious cleaning work. By sampling notebooks from GitHub and analyzing changes between subsequent commits, we identified common cleaning activities, such as changes to markdown (e.g., adding headers sections or descriptions) or comments (both deleting dead code and adding descriptions) as well as reordering cells. We also find that common cleaning activities differ depending on the intended purpose of the notebook. Our results provide a valuable foundation for tool builders and notebook users, as many identified cleaning activities could benefit from codification of best practices and dedicated tool support, possibly tailored depending on intended use.

2021-11-15

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) (published)